public int increment (int featureIndex, int incr) { if (featureIndex < 0 || featureIndex > alphabet.size()) throw new IllegalArgumentException ("featureIndex "+featureIndex+" out of range"); return featureCounts.adjustOrPutValue(featureIndex, incr, incr); }
public int increment (int featureIndex) { if (featureIndex < 0 || featureIndex > alphabet.size()) throw new IllegalArgumentException ("featureIndex "+featureIndex+" out of range"); return featureCounts.adjustOrPutValue(featureIndex, 1, 1); }
public int increment (int featureIndex, int incr) { if (featureIndex < 0 || featureIndex > alphabet.size()) throw new IllegalArgumentException ("featureIndex "+featureIndex+" out of range"); return featureCounts.adjustOrPutValue(featureIndex, incr, incr); }
public int increment (int featureIndex) { if (featureIndex < 0 || featureIndex > alphabet.size()) throw new IllegalArgumentException ("featureIndex "+featureIndex+" out of range"); return featureCounts.adjustOrPutValue(featureIndex, 1, 1); }
public int increment (int featureIndex, int incr) { if (featureIndex < 0 || featureIndex > alphabet.size()) throw new IllegalArgumentException ("featureIndex "+featureIndex+" out of range"); return featureCounts.adjustOrPutValue(featureIndex, incr, incr); }
public int increment (int featureIndex) { if (featureIndex < 0 || featureIndex > alphabet.size()) throw new IllegalArgumentException ("featureIndex "+featureIndex+" out of range"); return featureCounts.adjustOrPutValue(featureIndex, 1, 1); }
public int increment (Object entry, int incr) { return featureCounts.adjustOrPutValue(alphabet.lookupIndex(entry), incr, incr); }
public int increment (Object entry, int incr) { return featureCounts.adjustOrPutValue(alphabet.lookupIndex(entry), incr, incr); }
public int increment (Object entry) { return featureCounts.adjustOrPutValue(alphabet.lookupIndex(entry), 1, 1); }
public int increment (Object entry, int incr) { return featureCounts.adjustOrPutValue(alphabet.lookupIndex(entry), incr, incr); }
public int increment (Object entry) { return featureCounts.adjustOrPutValue(alphabet.lookupIndex(entry), 1, 1); }
public int increment (Object entry) { return featureCounts.adjustOrPutValue(alphabet.lookupIndex(entry), 1, 1); }
public void incrementTransition(Transducer.TransitionIterator ti, double count) { State source = (CRF.State) ti.getSourceState(); FeatureVector input = (FeatureVector) ti.getInput(); int index = ti.getIndex(); int nwi = source.weightsIndices[index].length; for (int wi = 0; wi < nwi; wi++) { int weightsIndex = source.weightsIndices[index][wi]; for (int i = 0; i < input.numLocations(); i++) { int featureIndex = input.indexAtLocation(i); weightCounts[weightsIndex].adjustOrPutValue(featureIndex, 1, 1); } } }
public Instance pipe(Instance instance) { TIntIntHashMap localCounter = new TIntIntHashMap(); if (instance.getData() instanceof FeatureSequence) { FeatureSequence features = (FeatureSequence) instance.getData(); for (int position = 0; position < features.size(); position++) { localCounter.adjustOrPutValue(features.getIndexAtPosition(position), 1, 1); } } else { throw new IllegalArgumentException("Looking for a FeatureSequence, found a " + instance.getData().getClass()); } for (int feature: localCounter.keys()) { counter.increment(feature); } numInstances++; return instance; }
public Instance pipe(Instance instance) { TIntIntHashMap localCounter = new TIntIntHashMap(); if (instance.getData() instanceof FeatureSequence) { FeatureSequence features = (FeatureSequence) instance.getData(); for (int position = 0; position < features.size(); position++) { localCounter.adjustOrPutValue(features.getIndexAtPosition(position), 1, 1); } } else { throw new IllegalArgumentException("Looking for a FeatureSequence, found a " + instance.getData().getClass()); } for (int feature: localCounter.keys()) { counter.increment(feature); } numInstances++; return instance; }
public Instance pipe(Instance instance) { TIntIntHashMap localCounter = new TIntIntHashMap(); if (instance.getData() instanceof FeatureSequence) { FeatureSequence features = (FeatureSequence) instance.getData(); for (int position = 0; position < features.size(); position++) { localCounter.adjustOrPutValue(features.getIndexAtPosition(position), 1, 1); } } else { throw new IllegalArgumentException("Looking for a FeatureSequence, found a " + instance.getData().getClass()); } for (int feature: localCounter.keys()) { counter.increment(feature); } numInstances++; return instance; }
public void addInstances (InstanceList training, List<LabelSequence> topics) { initializeForTypes (training.getDataAlphabet()); assert (training.size() == topics.size()); for (int i = 0; i < training.size(); i++) { Topication t = new Topication (training.get(i), this, topics.get(i)); data.add (t); // Include sufficient statistics for this one doc FeatureSequence tokenSequence = (FeatureSequence) t.instance.getData(); LabelSequence topicSequence = t.topicSequence; for (int pi = 0; pi < topicSequence.getLength(); pi++) { int topic = topicSequence.getIndexAtPosition(pi); typeTopicCounts[tokenSequence.getIndexAtPosition(pi)].adjustOrPutValue(topic, 1, 1); tokensPerTopic[topic]++; } } initializeHistogramsAndCachedValues(); }
public void addInstances (InstanceList training, List<LabelSequence> topics) { initializeForTypes (training.getDataAlphabet()); assert (training.size() == topics.size()); for (int i = 0; i < training.size(); i++) { Topication t = new Topication (training.get(i), this, topics.get(i)); data.add (t); // Include sufficient statistics for this one doc FeatureSequence tokenSequence = (FeatureSequence) t.instance.getData(); LabelSequence topicSequence = t.topicSequence; for (int pi = 0; pi < topicSequence.getLength(); pi++) { int topic = topicSequence.getIndexAtPosition(pi); typeTopicCounts[tokenSequence.getIndexAtPosition(pi)].adjustOrPutValue(topic, 1, 1); tokensPerTopic[topic]++; } } initializeHistogramsAndCachedValues(); }
public void addInstances (InstanceList training, List<LabelSequence> topics) { initializeForTypes (training.getDataAlphabet()); assert (training.size() == topics.size()); for (int i = 0; i < training.size(); i++) { Topication t = new Topication (training.get(i), this, topics.get(i)); data.add (t); // Include sufficient statistics for this one doc FeatureSequence tokenSequence = (FeatureSequence) t.instance.getData(); LabelSequence topicSequence = t.topicSequence; for (int pi = 0; pi < topicSequence.getLength(); pi++) { int topic = topicSequence.getIndexAtPosition(pi); typeTopicCounts[tokenSequence.getIndexAtPosition(pi)].adjustOrPutValue(topic, 1, 1); tokensPerTopic[topic]++; } } initializeHistogramsAndCachedValues(); }
public double[][] getWordFrequencies() { if (instances == null) { throw new IllegalStateException("You must load instances before you can count features"); } double[][] result = new double[ numTypes ][ 2 ]; TIntIntHashMap docCounts = new TIntIntHashMap(); for (Instance instance: instances) { FeatureSequence features = (FeatureSequence) instance.getData(); for (int i=0; i<features.getLength(); i++) { docCounts.adjustOrPutValue(features.getIndexAtPosition(i), 1, 1); } int[] keys = docCounts.keys(); for (int i = 0; i < keys.length; i++) { int feature = keys[i]; result[feature][0] += docCounts.get(feature); result[feature][1]++; } docCounts = new TIntIntHashMap(); } return result; }